CPython can be fast once you eliminate the main bottleneck: the interpreter itself.
Processing loops adds a lot of overhead because the interpreter has to make the case jumps after each loop. Figuring out a way to minimize that overhead by using built-in data structures and stdlib library will speed up your code by an order of magnitude.
Don't forget, the built-in types are already running in C.
I mean, yes, but that's probably not viable in many real-world applications. Usually you have a pretty complex data model (some graph-like structure) with Python methods that traverse it. You can't easily push that into native Python structures (at least not with any significant performance gain), and while you can push the whole thing into Rust or some faster language, at that point all of the interesting stuff is happening in Rust so why use Python at all (why pay the significant costs of a hybrid application)?
Processing loops adds a lot of overhead because the interpreter has to make the case jumps after each loop. Figuring out a way to minimize that overhead by using built-in data structures and stdlib library will speed up your code by an order of magnitude.
Don't forget, the built-in types are already running in C.